2026-04-17T20:25:53Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/114852026-03-21T01:05:42Zcom_10259_3847com_10259_5086com_10259_2604col_10259_7109

Redondo Guevara, Raquel Santos González, Pedro Bustillo Iglesias, Andrés Sedano, Javier Villar, José R. Correa, Maritza Alique, José Ramón Corchado, Emilio 2026-03-20T13:52:32Z 2026-03-20T13:52:32Z 2009 978-3-642-02480-1 978-3-642-02481-8 https://hdl.handle.net/10259/11485 10.1007/978-3-642-02481-8_190 Comunicación presentada en: 10th International Work-Conference on Artificial Neural Networks, IWANN 2009 Workshops, Salamanca, Spain, June 10-12, 2009. Proceedings, Part II In this paper we present a soft computing system developed to optimize the face milling operation under High Speed conditions in the manufacture of steel components like molds with deep cavities. This applied research presents a multidisciplinary study based on the application of neural projection models in conjunction with identification systems, in order to find the optimal operating conditions in this industrial issue. Sensors on a milling centre capture the data used in this industrial case study defined under the frame of a machine-tool that manufactures industrial tools. The presented model is based on a two-phase application. The first phase uses a neural projection model capable of determine if the data collected is informative enough. The second phase is focus on identifying a model for the face milling process based on low-order models such as Black Box ones. The whole system is capable of approximating the optimal form of the model. Finally, it is shown that the Box-Jenkins algorithm, which calculates the function of a linear system from its input and output samples, is the most appropriate model to control such industrial task for the case of steel tools. This research has been partially supported through project BU006A08 of Junta de Castilla y León, through projects CIT-020000-2008-2 and TIN2007-62626 of Spanish Ministry of Science and Innovation and the MOCAVE Project under Grant DPI2006- 12736-C02-01. The authors would also like to thank the manufacturer of components for vehicle interiors, Grupo Antolin Ingeniería, S.A. in the framework of the project MAGNO 2008 - 1028.- CENIT Project funded by the Spanish Ministry of Science and Innovation. application/pdf eng Springer Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, p. 1282–1291 https://doi.org/10.1007/978-3-642-02481-8_190 Fresado Redes neuronales artificiales Milling (Metal-work) Neural networks (Computer science) A Soft Computing System to Perform Face Milling Operations info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/acceptedVersion info:eu-repo/semantics/openAccess 5518 1282 1291